These days, imbalanced datasets, denoted throughout the paper by ID, (a dataset that contains some (usually two) classes where one considerably smaller number of samples than other(s)) emerge in many real world problems (like health care systems or disease diagnosis systems, anomaly detection, fraud stream based malware detection and so on) these datasets cause under-training minority class(es)...

A support vector machine constructs an optimal hyperplane from a small set of samples near the boundary. This makes it sensitive to these specific samples and tends to result in machines either too complex with poor generalization ability or too imprecise with high training error, depending on the kernel parameters. In this paper, we present an improved version of the method, called editing sup...

Support vector machines (SVMs) are a class of popular classification algorithms for their high generalization ability. However, it is time-consuming to train SVMs with a large set of learning samples. Improving learning efficiency is one of most important research tasks on SVMs. It is known that although there are many candidate training samples in some learning tasks, only the samples near pla...

about 35 samples were collected from lower and upper parts of the proposed frasnianamennian boundary in three sections of upper devonian marine sediments of the kerman province. the biostratigraphical and geochemical studies indicate that about 45% extinction of brachiopods probably caused by the frasnian-famennian bioevents, which is slightly higher than thr rate of devonian-carboniferous exti...

The problem of uncertainty quantification (UQ) for inverse problems has become of significant recent interest. However, UQ requires more than the classical methods for computing solutions of inverse problems. In this paper, we take a Bayesian approach for the solution of ill-posed deconvolution problems with a symmetric convolution kernel and Neumann boundary conditions. The prior is modeled as...

Support vector machine (SVM) is one of the most powerful supervised learning algorithms in gene expression analysis. The samples intermixed in another class or in the overlapped boundary region may cause the decision boundary too complex and may be harmful to improve the precise of SVM. In the present paper, hybridized k-nearest neighbor (KNN) classifiers and SVM (HKNNSVM) is proposed to deal w...

Single-crystal Ni-base superalloys, consisting of a two-phase γ/ γ ' microstructure, retain high strengths at elevated temperatures and are key materials for high temperature applications, like, e.g., turbine blades of aircraft engines. The lattice misfit between the γ and γ ' phases results in internal stresses, which significantly influence the deformation and creep behavior of the material. ...